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BDE SC3.3 Workshop - Options for Wind Farm performance assessment and Power forecasting

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Options for Wind Farm performance assessment and Power forecasting (Mr. A. Kyritsis, ALTSOL/TERNA) at the BigDataEurope Workshop, Amsterdam, Novermber 2017.

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BDE SC3.3 Workshop - Options for Wind Farm performance assessment and Power forecasting

  1. 1. Options for Wind Farm performance assessment and Power Forecasting 2017-11-28 Athanasios Kyritsis Software Engineer & Analyst Alternative Solutions Ltd (AltSol) Co-author: Antonios Papoutsakis (Terna Energy)
  2. 2. Overview  Introductions  Data Management & Forecasting Tools  Technologies Involved  Future Challenges and Needs  Conclusions
  3. 3. Who am I?  Thanos Kyritsis  Computer Science from The Open University, UK Electrical & Computer Engineering from University of Patras, Greece  Started working for AltSol in 2007 Co-owner of AltSol since 2012  Operationally responsible and Lead Engineer/Analyst for Terna Energy meteo-wind-data software application platform  Born in 1982 and lives in Athens, Greece
  4. 4. AltSol Portfolio  Software-house company established in Athens, Greece  SME, employs 10 people  Terna Energy partner since 2009  Specializes in developing custom-made software, enterprise applications, backend infrastructure, also offering IT-services consulting, operational, technical/user support  Open-source enthusiasts  Operational since 2000
  5. 5. Terna Energy Wind Portfolio Currently 39 wind farms of 969 MW capacity are operating in Greece, Bulgaria, Poland and USA
  6. 6. Integrated Data Management Customized Reporting Visualization Monitoring Integrate New Tools
  7. 7. Power Forecasting Tool  The next slide shows the tool’s inputs and outputs workflow  The Data Storage infrastructure has a central role  Data Management needs to be equally efficient and flexible as the rest of the platform  Real-time calculation functions, transformation and combination actions are necessary  Abstraction is achieved by the time-series pre-processor  Visualization modules range from simple display of time-series, placing coordinates on a map or real-time manipulating the data (tags, aggregations, filters on the time-series)
  8. 8. Power Forecasting Tool
  9. 9. 9 Case Study: Portfolio Forecasting • 9 Wind Farms in different areas of Greece • Portfolio Capacity 242 MW • Lowest capacity 8MW, highest capacity 73.2MW • Hourly day ahead forecasts • Test Period 1st July – 1st October 2017 • Distances ~ 1-400Km from each other WF 6 WF 7 WF 8 WF 9 WFs 1-5
  10. 10. Overall Application Architecture  The next slide shows the platform’s structural architecture  Follows the modular approach in order to manage the diversity in TERNA’s ecosystem and multiple data formats and versions  Each module needs the ability to interconnect and exchange data with external or internal systems (written in different languages or run in different environments)  The previous’ slide I/O workflow is depicted in this diagram as the “Prediction & Forecasting module”, as a reference for each module’s complexity  The Backend Application & Business Logic is the central controller or “glue” that makes all the modules work with each other, with external/internal system and as a whole
  11. 11. Overall Application Architecture
  12. 12. Application Integrated Technologies  Java EE Groovy Javascript R  PostgreSQL PL/SQL PL/R  RedHat JBoss AS Apache Web Server BIRT
  13. 13. Technologies under Consideration  Docker Ecosystem  Apache Cassandra Neo4J & Graph DBs Apache Hadoop Apache Spark Apache Flink Apache Hive  Node.JS
  14. 14. Future Challenges and Needs  Volume increase management & Data partitioning  Real-time timeseries transformations  Semantics, Relationships and Metadata handling for abstract implementations  Making data manipulation simple for RES operators and engineers  Combining multiple data formats and input sources  Pattern recognition among different datasets  Technology maturity, hype & decline - How to invest & embrace
  15. 15. Conclusions  An Integrated Data Management system allows RES operators to extract value from their data  Aggregated (scaled-up) data components improve forecasting accuracy, although effectively dependent on their individual assets’ correlations  Big Data is not just about volume and machine learning in general, but also exposing proper information towards its relevant target group in a timely and appropriately- formed manner
  16. 16. Thank you for your attention! Copyright notice. This document has been created by AltSol Ltd and Terna Energy S.A. and contains copyrighted material, trademark, and other proprietary information. All rights reserved. Please contact us for further Q&A Athanasios Kyritsis: thanos@altsol.gr Antonios Papoutsakis: apapoutsakis@terna-energy.com Terna Energy S.A.: http://www.terna-energy.com AltSol Ltd: https://www.linkedin.com/company/altsol-ltd/ Application demo: http://meteo-wind-data.altsol.gr

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